Delimiting the Sydney speech community

1987 ◽  
Vol 16 (2) ◽  
pp. 179-204 ◽  
Author(s):  
Barbara Horvath ◽  
David Sankoff
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Quantitative Methods ◽  
Language Change ◽  
Large Data ◽  
Social Characteristics ◽  
Speech Community ◽  
Components Analysis ◽  
Australian English ◽  
Linguistic Behaviour

ABSTRACTQuantitative analyses of large data sets make use of both linguistic and sociological categories in sociolinguistic studies. While the linguistic categories are generally well-defined and there are sufficient tokens for further definition based on mathematical manipulation, the social characteristics such as socioeconomic class or ethnicity are neither. The familiar problem of grouping speakers by such sociological characteristics prior to quantitative analysis is addressed and an alternative solution – principal components analysis – is suggested. Principal components analysis is used here as a heuristic for grouping speakers solely on the basis of linguistic behaviour; the groups thus defined can then be described according to sociological characteristics. In addition, by naming the principal components, the major linguistic and social dimensions of the variation in the data can be identified. Principal components analysis was applied to vowel variation data collected as part of a sociolinguistic survey of English in Sydney, New South Wales, Australia. (Sociolinguistics, variation studies, quantitative methods in linguistics, dialectology, Australian English, role of migrants in language change)

A Comparison of Two Quantitative Methods of Analysing Thyroid Dynamic Studies in Nuclear Medicine

1979 ◽  
Vol 65 (1) ◽  
pp. 29-32
Author(s):  
A. S. Houston ◽  
N. R. Thorpe
Keyword(s):  
Nuclear Medicine ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Quantitative Methods ◽  
Physiological Model ◽  
Small Sample ◽  
Good Separation ◽  
Model Based ◽  
Dynamic Studies ◽  
Components Analysis

AbstractTwo quantitative methods of analysing thyroid dynamic studies using 99mTc O-4 are compared for 33 patients (22 euthyroid, 8 hyperthyroid and 3 hypothyroid). A physiological model based on pertechnetate uptake by the thyroid is compared with principal components analysis. Both methods gave good separation between euthyroid and hvperthyroid in all but one case, while the separation between euthyroid and hypothyroid, although based on a small sample, appeared to be better for principal components analysis.

scholarly journals Haplotype Classification Using Copy Number Variation and Principal Components Analysis

2013 ◽  
Vol 7 (1) ◽  
pp. 19-24
Author(s):  
Kevin Blighe
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Large Scale ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Reduction Techniques ◽  
Number Variation ◽  
Components Analysis

Elaborate downstream methods are required to analyze large microarray data-sets. At times, where the end goal is to look for relationships between (or patterns within) different subgroups or even just individual samples, large data-sets must first be filtered using statistical thresholds in order to reduce their overall volume. As an example, in anthropological microarray studies, such ‘dimension reduction’ techniques are essential to elucidate any links between polymorphisms and phenotypes for given populations. In such large data-sets, a subset can first be taken to represent the larger data-set. For example, polling results taken during elections are used to infer the opinions of the population at large. However, what is the best and easiest method of capturing a sub-set of variation in a data-set that can represent the overall portrait of variation? In this article, principal components analysis (PCA) is discussed in detail, including its history, the mathematics behind the process, and in which ways it can be applied to modern large-scale biological datasets. New methods of analysis using PCA are also suggested, with tentative results outlined.

An intonational change in progress in Australian English

1986 ◽  
Vol 15 (1) ◽  
pp. 23-51 ◽  
Author(s):  
Gregory Guy ◽  
Barbara Horvath ◽  
Julia Vonwiller ◽  
Elaine Daisley ◽  
Inge Rogers
Keyword(s):  
Quantitative Methods ◽  
English Language ◽  
Language Change ◽  
Speech Community ◽  
Local Identity ◽  
Time Data ◽  
Sociolinguistic Variation ◽  
The Social ◽  
Social Motivations ◽  
Australian English

ABSTRACTMany speakers of current Australian English often use a high-rising intonation in statements. This usage, which has been termed Australian Questioning Intonation (AQI), has a nonpropositional, interactive meaning (checking for listener comprehension) and interacts with the turn-taking mechanism of conversation. A quantitative study of the use of AQI in Sydney reveals that it has the social distribution characteristic of a language change in progress: higher rates of usage among working-class speakers, teenagers, and women. Real time data confirm this, showing that the form was almost nonexistent in this speech community two decades earlier. The social motivations of this innovation are examined in terms of local identity and the entry of new ethnic groups into the community, and possible linguistic sources are discussed. The utility of quantitative methods in studying meaningful linguistic variables is demonstrated. (Australian English, language change in progress, intonation, sociolinguistic variation, social class, social motivation)

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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